Binarization is a classical research problem for document analysis and many document binarization methods have been proposed over the past two decades in the literature.
It aims to convert each pixel in a document image into either text or background.
The most popular and simple method is the Otsu [1]}, which is a nonparametric and unsupervised method of automatic threshold selection approach for gray-scale image binarization.
It selects the global threshold based on the gray-scale histogram without any priori knowledge thus the computational complexity is linear.
The Otsu method works very well on uniform and clean images while produces poor results on degraded document images with nonuniform background.
In order to solve this problem, local adaptive threshold methods have been proposed, such as Sauvola [2]}, Niblack [3]}, Pai [4]} and AdOtsu [5]}, [6]}.
These methods compute the local threshold for each pixel based on the local statistic information, such as the mean and standard deviation of a local area around the pixel.
It should be noted that binarization is not always the goal.
Methods such as Otsu can also be used for strong contrast enhancement.
